1887
Volume 65 Number 1
  • E-ISSN: 1365-2478

Abstract

ABSTRACT

We introduce the signal dependent time–frequency distribution, which is a time–frequency distribution that allows the user to optimize the tradeoff between joint time–frequency resolution and suppression of transform artefacts. The signal‐dependent time–frequency distribution, as well as the short‐time Fourier transform, Stockwell transform, and the Fourier transform are analysed for their ability to estimate the spectrum of a known wavelet used in a tuning wedge model. Next, the signal‐dependent time–frequency distribution, and fixed‐ and variable‐window transforms are used to estimate spectra from a zero‐offset synthetic seismogram. Attenuation is estimated from the associated spectral ratio curves, and the accuracy of the results is compared. The synthetic consisted of six pairs of strong reflections, based on real well‐log data, with a modeled intrinsic attenuation value of 1000/ = 20. The signal‐dependent time–frequency distribution was the only time–frequency transform found to produce spectra that estimated consistent attenuation values, with an average of 1000/ = 26±2; results from the fixed‐ and variable‐window transforms were 24±17 and 39±10, respectively. Finally, all three time–frequency transforms were used in a pre‐stack attenuation estimation method (the pre‐stack inversion algorithm) applied to a gather from a North Sea seismic dataset, to estimate attenuation between nine different strong reflections. In this case, the signal‐dependent time‐frequency distribution produced spectra more consistent with the constant‐Q model of attenuation assumed in the pre‐stack attenuation estimation algorithm: the average L1 residuals of the spectral ratio surfaces from the theoretical constant‐Q expectation for the signal‐dependent time‐frequency distribution, short‐time Fourier transform, and Stockwell transform were 0.12, 0.21, and 0.33, respectively. Based on the results shown, the signal‐dependent time‐frequency distribution is a time–frequency distribution that can provide more accurate and precise estimations of the amplitude spectrum of a reflection, due to a higher attainable time–frequency resolution.

Loading

Article metrics loading...

/content/journals/10.1111/1365-2478.12407
2016-07-12
2024-04-24
Loading full text...

Full text loading...

References

  1. BathM.1974. Spectral Analysis in Geophysics, Vol. 7 (Developments in Solid Earth Geophysics). Elsevier Scientific Publishing Company.
    [Google Scholar]
  2. BeckwithJ. and ClarkR.2014. Improved spectral estimates for attenuation studies. In: 76th EAGE Conference and Exhibition 2014.
  3. BoashashB.2003. Time Frequency Signal Analysis and Processing: A Comprehensive Reference. Elsevier Science Limited.
    [Google Scholar]
  4. CastagnaJ., SunS. and SiegfriedR.2003. Instantaneous spectral analysis: detection of low‐frequency shadows associated with hydrocarbons. The Leading Edge22, 120–127.
    [Google Scholar]
  5. DasguptaR. and ClarkR.1998. Estimation of q from surface seismic reflection data. Geophysics63, 2120–2128.
    [Google Scholar]
  6. de Bruijn, N.1973. A theory of generalized functions, with applications to Wigner distributions and Weyl correspondence. Nieuw Archief voor Wiskunde21(3), 205–280.
    [Google Scholar]
  7. FlandrinP.1999. time–frequency/Time‐Scale Analysis. Academic Press.
    [Google Scholar]
  8. GaborD.1946. Theory of communication. part 1: the analysis of information. Journal of the Institution of Electrical Engineers ‐ Part III: Radio and Communication Engineering93, 429–441.
    [Google Scholar]
  9. HallM.2006a. Predicting bed thickness with cepstral decomposition. The Leading Edge25, 199–204.
    [Google Scholar]
  10. HallM.2006b. Resolution and uncertainty in spectral decomposition. First Break24, 43–47.
    [Google Scholar]
  11. KoenigW., DunnH.K. and LacyL.Y.1946. The sound spectrograph. The Journal of the Acoustical Society of America18, 19–49.
    [Google Scholar]
  12. LinerC.L.2012. Elements of Seismic Dispersion: A Somewhat Practical Guide to Frequency‐Dependent Phenomena . Society of Exploration Geophysicists.
  13. ReineC., ClarkR. and van der BaanM.2012. Robust prestack Q determination using surface seismic data: Part 1‐Method and synthetic examples. Geophysics77, R45–R56.
    [Google Scholar]
  14. ReineC., van der BaanM. and ClarkR.2009. The robustness of seismic attenuation measurements using fixed‐ and variable‐window time–frequency transforms. Geophysics74, WA123–WA135.
    [Google Scholar]
  15. RyanH.1994. Ricker, ormsby, klauder, butterworth ‐ a choice of wavelets. CSEG Recorder19.
    [Google Scholar]
  16. SinhaS.K., RouthP.S., AnnoP.D. and CastagnaJ.P.2003. Time frequency attribute of seismic data using continuous wavelet transform. 73rd SEG meeting, Dallas, USA, Expanded Abstracts, 1481–1484.
    [Google Scholar]
  17. StockwellR., MansinhaL. and LoweR.1996. Localization of the complex spectrum: the S transform. IEEE Transactions on Signal Processing44, 998–1001.
    [Google Scholar]
  18. TanerM.T., KoehlerF. and SheriffR.E.1979. Complex seismic trace analysis. Geophysics44, 1041–1063.
    [Google Scholar]
  19. TonnR.1991. The determination of the seismic quality factor q from vsp data: a comparison of different computational methods. Geophysical Prospecting39, 1–27.
    [Google Scholar]
  20. van der BaanM.2001. Acoustic wave propagation in one dimensional random media: the wave localization approach. Geophysical Journal International145, 631–646.
    [Google Scholar]
  21. VilleJ.1948. Théorie et applications de la notion de signal analytique. Cables et Transmissions2(1), 61–74.
    [Google Scholar]
  22. WangY.2007. Seismic time–frequency spectral decomposition by matching pursuit. Geophysics72, V13–V20.
    [Google Scholar]
  23. ZhangK., MarfurtK.J., SlattR.M. and GuoY.2009. Spectral decomposition illumination of reservoir facies. 79th SEG meeting, Houston, USA, Expanded Abstracts3515–3519.
  24. ZhaoB., JohnstonD. and GouveiaW.2006. Spectral decomposition of 4D seismic data. 76th SEG meeting, New Orleans, USA, Expanded Abstracts, 3235–3239.
http://instance.metastore.ingenta.com/content/journals/10.1111/1365-2478.12407
Loading
/content/journals/10.1111/1365-2478.12407
Loading

Data & Media loading...

  • Article Type: Research Article
Keyword(s): Attenuation; Signal processing

Most Cited This Month Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error